In industrial vehicles, such as dump trucks, a reduction in the down time at the time of a failure is important; therefore, several conventional failure diagnosis apparatuses have been proposed. These failure diagnosis apparatuses are to diagnose which device or portion of a device fails when the vehicle develops any trouble. For example, they diagnose one by one whether or not operations and functions of respective sensors, respective actuators, respective controllers, and the like, are normal; and when an error is detected they use an indicator to inform an operator of the name of a failed part and a code number indicative of the kind of abnormality. Since the failed part with an error indication can be replaced or repaired, the recovery can be completed within a short period of time.
Such conventional failure diagnosis apparatuses may, however, cause a problem in that when a failure occurs intermittently without showing a continuous abnormal state, a true cause of the failure cannot be detected. In such a case, a cause of the failure or the failed part is guessed from the contents of the failure, and some recovery steps, such as a replacement of an associated part or sensor, are taken to recover from the failure. However, if the recovery steps are irrelevant, the part replacement must be repeated many times, and it takes a long time to diagnose a true cause of the failure. Such an extended idle time reduces the efficiency of the failure correction, causing a problem in that the down time cannot be reduced.
Further, recently there has been a strong demand to develop a failure diagnosis apparatus for use not only to correct a failure when an error occurs, but also to predict a failure before an error occurs, so as to prevent the generation of the failure and hence to reduce the down time. Such a failure diagnosis apparatus periodically diagnoses the states of respective devices, such as an engine, a transmission, axles, suspensions, hydro-pneumatic systems, and a brake, stores the diagnostic data as hysteresis, and predicts an occurrence of a failure on the basis of the hysteresis. In other words, it performs a so-called trend analysis. Even when a failure has occurred, the failure diagnosis apparatus can refer to the hysteresis data to diagnose a true cause of the failure.
However, the specific work of such periodic diagnoses requires an interruption of vehicle operations. This causes an undesirable reduction in the operating ratio of the vehicle, and hence is a hindrance to production efficiency. Therefore, it is desirable to provide a diagnosis method which is capable of being carried out without any interruption of vehicle operations. In this diagnosis method, the diagnostic data to be stored as hysteresis must be pertinent data which is capable of diagnosing an exact failure portion, i.e., capable of representing a state of a trend toward the generation of a failure.